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All Journal Jurnal Teknologi Terpadu Jurnal Informatika Terpadu The Indonesian Journal of Computer Science DBESTI: Journal of Digital Business and Technology Innovation
adriansyah, ahmad rio
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science

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Journal : Jurnal Informatika Terpadu

 1 
Analisis dan Pengembangan Sistem Informasi Pengelolaan Masjid berbasis Mobile dengan Teknologi API Web Service Mujahid, Ahmad; Abdullah, Muhammad Yahya; Suharya, Suharya; Adriansyah, Ahmad Rio
Jurnal Informatika Terpadu Vol 7 No 2 (2021): September, 2021
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v7i2.368

Abstract

YukAmal (yukamal.com) is a web-based mosque information and management system. Feature on YukAmal website is a donation, finance, construction progress, and information mosques. At present, the application cannot integrate with mobile apps. The research aims to design the YukAmal application based on Android Kotlin, integrated using REST API web service technology. The method applied in this research uses Scrum to get optimal results. This application divide into three modules: Information and Mosque Search, Donation and Mosque Finance, and REST API Web Service. The method used is UAT (User Acceptance Testing). For web service, REST API feature gets 85% test results, mosque information features and online donations get 80% test results, financial report feature gets 25% test results or can only view financial information. Results of the research, this application was proper for use by worshipers and mosque administrators in Depok City.
Analisis dan Perancangan Aplikasi Penganggaran Barang berbasis Web pada Unit Sarana Prasarana Perguruan Tinggi Maharani, Siti Zahra; Adriansyah, Ahmad Rio
Jurnal Informatika Terpadu Vol 8 No 1 (2022): Maret, 2022
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v8i1.402

Abstract

This study discusses the budget approval process at the Nurul Fikri Integrated Technology High School Facilities and Infrastructure Unit, which is inflexible because it requires a physical supervisor's signature, management of the procurement of goods that have not been carried out centrally and is expected to reduce delays in the fulfillment of goods and repetitive work. Therefore, an STT-NF Goods Budgeting application is needed to facilitate submissions and streamline time in meeting the needs of goods and data backup. The method used in designing the STT-NF Goods Budgeting application uses a literature review, interviews with the head of the Integrated High School Facilities and Infrastructure Section, Nurul Fikri, and the Incremental Development System method in stages. The study results showed the suitability of the features as expected; 96% of users stated that the features in the information system were by the process of submitting existing goods.
Pengembangan Sistem Deteksi Tuberkulosis pada Citra X-Ray Menggunakan Metode Convolutional Neural Network (CNN) dengan Framework Laravel Alimi, Aldi Akbar; Adriansyah, Ahmad Rio; Prima, Pudy
Jurnal Informatika Terpadu Vol 10 No 2 (2024): September, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v10i2.1437

Abstract

Tuberculosis or TB is a disease caused by Mycobacterium Tuberculosis, which has a high transmission level. TB disease can be diagnosed through several methods, namely, using sputum samples and using x-ray scans. However, both methods take a long time to detect. Therefore, a detection system is needed to detect TB disease quickly and can be done by anyone. This research creates a detection system that can detect TB disease through chest x-ray images. The detection system is a web-based application built using the Laravel framework and a machine learning model with the Convolutional Neural Network (CNN) method for X-ray image analysis. This research will apply the CNN model that has been made into a web-based application through an API created using the FastAPI framework. The results of research on the detection system show that the detection system can detect TB disease. Proven by the results of testing conducted using the black box testing method, the test results show that the test success rate is 87%. In addition, the machine learning model with the CNN method can also provide classification on x-ray images well, where an accuracy of 93% is obtained on training data and 85% on test data.
Co-Authors Abdullah, Muhammad Yahya Ahmad Mujahid, Ahmad Al Fajri, Fikri Pratama Al Khairi, Said Alimi, Aldi Akbar Atmam Al Faruqi, Hamdan Ainul Edi Wibowo Ishom Muhammad Drehem, Ishom Muhammad Lukman Rosyidi Maharani, Siti Zahra Muhammad Fikri Nabila, Zakiah Panji, Krisna Prasetyo, Kurniawan Dwi Prima, Pudy Sahrudin, Asep Sirojul Munir Suharya, Suharya